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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Ahmad Fahriyannur Rosyady
Politeknik Negeri Jember, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Efficient Intrusion Detection System Utilizing Ensemble Learning and Statistical Feature Selection in Agricultural IoT Networks Ahmad Fahriyannur Rosyady; Bekti Maryuni Susanto; Agus Hariyanto; Mukhamad Angga Gumilang
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.448

Abstract

To enhance agricultural processes, smart agriculture combines a variety of devices,protocols, computing paradigms, and technologies. The cloud, edge computing, big data, andartificial intelligence all offer tools and solutions for managing, storing, and analyzing the vastamounts of data produced by various parts. Smart agriculture is still in its infancy and lacks severalsecurity measures, brought in the creation of numerous networks that are vulnerable to cyberattacks.The most well-known cyberattack is called a denial of service (DoS) attack, in which the attackersoverwhelm the network with massive amounts of data or requests, preventing the nodes fromaccessing the various services that are provided in that network. Intrusion Detection Systems (IDS)have shown to be effective defense mechanisms in the event of a cyberattack. The implementationof conventional intrusion detection systems (IDS) approaches in Internet of Things (IoT) deviceswas hindered by resource constraints, such as reduced computing capacity and low powerconsumption. In this paper, we used an ensemble learning and statistical based feature selectionstrategy to create a lightweight intrusion detection solution. The results show that the stackingensemble method is able to improve the performance of single machine learning in the classificationof anomalous events even though the computation time required is quite large compared to thecomputation time of single machine learning
Multivariate LSTM with SLO-Aware Loss for Virtual Machine Workload Prediction on Cloud Data Center Agus Hariyanto; Ahmad Fahriyannur Rosyady; Adi Sucipto; Bekti Maryuni Susanto; Sapta Nugraha; Nicolas Chenu
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.490

Abstract

Accurate virtual machine (VM) workload prediction is a key component of cloud resource management, particularly to support auto-scaling and to maintain Service Level Objectives (SLOs). In conventional prediction models that rely on symmetric loss functions such as Mean Squared Error (MSE), under-prediction errors are treated equivalently to over-prediction errors, even though under-prediction carries significantly more severe operational consequences — it directly triggers capacity shortages and SLO violations. This study proposes a CPU workload prediction approach based on a multivariate Long Short-Term Memory (LSTM) network enhanced with an SLO-aware loss, an asymmetric loss function that penalizes under-prediction ten times more heavily than over-prediction. Experiments are conducted on a subset of 25,000 rows from the Bitbrain GWA-T-12 fastStorage dataset with four input features (CPU, memory, network received, network transmitted), using a fixed random seed for reproducibility. Two models are trained and compared: one with SLO-aware loss and one with standard MSE as baseline, both sharing identical architecture and hyperparameters. The primary evaluation metric is the under-prediction rate, which directly quantifies SLO violation risk. Results show that the SLO-aware model achieves an under-prediction rate of 0.04%, compared to 0.16% for the MSE baseline — a fourfold reduction. These findings empirically confirm that SLO-aware loss effectively directs the model toward conservative predictions that protect SLO compliance, establishing loss function design as a critical and actionable dimension in cloud VM workload prediction.
Co-Authors Adi Sucipto Agus Hariyanto Agus Hariyanto Bekti Maryuni Susanto Bekti Maryuni Susanto Mukhamad Angga Gumilang Nicolas Chenu Sapta Nugraha